Collaborative design of a care pathway for pharmacy-based PrEP delivery in Nigeria: insights from stakeholder consultation
Bibliographic record
Abstract
BACKGROUND: HIV remains a significant public health problem, particularly in Africa, where two-thirds of global cases occur. Nigeria is among the three countries with the highest burden. Despite free access to pre- and post-exposure prophylaxis (PrEP and PEP) in Nigerian hospitals, stigma, distance, and restrictive clinic hours hinder uptake, especially among vulnerable populations. Building on the successful pilot implementation of pharmacy-based PrEP delivery in Kenya, we engaged Nigerian stakeholders in adapting the model, addressing user and provider concerns to ensure effective implementation in Nigeria. METHODS: The stakeholder meeting took place in Abuja, Nigeria, which is selected for its central location and accessibility to various stakeholders, particularly those involved in HIV prevention efforts. The participants were purposefully selected to ensure diverse representations, including youth who are potential PrEP users, pharmacy providers, regulators, and representatives from civil society organizations. The meeting utilized the Nominal Group Technique (NGT)-a structured method for facilitating group decision-making and prioritizing ideas-to adapt the Kenyan pharmacy-delivered PrEP model for implementation in the Nigerian context. Mock role play was conducted to help participants understand the care pathway. The discussions culminated in identifying challenges and viable strategies for implementing the model in Nigeria. RESULTS: The one-day stakeholder meeting on 9 October 2024 was attended by 20 participants from various sectors involved in HIV prevention services. Stakeholders expressed enthusiasm for pharmacy-based PrEP delivery while acknowledging challenges associated with clinic-based services, such as stigma, limited hours, and long wait times. The key recommendations included training pharmacy providers, increasing awareness, ensuring confidentiality, establishing referral linkages, and integrating program data into the Health Management Information System (HMIS) as well as ensuring commodity availability and access. To enhance the success of the pilot study, stakeholders proposed engaging a research assistant, forming a monitoring team, and submitting the results to the Pharmacy Council of Nigeria (PCN) for review. CONCLUSIONS: The identified challenges and strategies for implementing the model in Nigeria will inform the development of a refined pharmacy-delivered PrEP framework that is ready for pilot testing and potential scaling across the country.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".